In our model, we use a system of ordinary differential equations (ODEs) for Susceptible-Infected-Recovered-Dead (SIRD) epidemic modeling, Particle Swarm Optimization (PSO) for model parameter optimization, and stacked-LSTM for forecasting the model parameters.
Outcomes validate that our proposed model shows greatly enhanced performance as compared to the existent unsupervised state-of-the-art approaches.
Ranked #1 on Extractive Text Summarization on DUC 2004
To address information overload in COVID-19 scientific literature, the study presents a novel hybrid model named CovSumm, an unsupervised graph-based hybrid approach for single-document summarization, that is evaluated on the CORD-19 dataset.
Ranked #1 on Unsupervised Text Summarization on CORD-19
Covid-19 has spread across the world and several vaccines have been developed to counter its surge.
A novel transformer model is proposed in this paper for click prediction and relevance estimation that learns additionally from the vertical information, apart from the query and search engine results that are the inputs for the traditional click models.
To tackle the problem of bias towards majority classes, researchers have presented various techniques to oversample the minority class data points.
The contributions of our paper are as follows: 1) An emotion detector module trained on the input utterances determines the affective state of the user in the initial phase 2) A novel transformer encoder is proposed that adds and normalizes the word embedding with emotion embedding thereby integrating the semantic and affective aspects of the input utterance 3) The encoder and decoder stacks belong to the Transformer-XL architecture which is the recent state of the art in language modeling.
In this paper, we have presented a novel deep neural network architecture involving transfer learning approach, formed by freezing and concatenating all the layers till block4 pool layer of VGG16 pre-trained model (at the lower level) with the layers of a randomly initialized naïve Inception block module (at the higher level).
In our approach we tried to improve the baseline accuracy from 9. 34% by using stemming, phoneme extraction, filtering and pruning.
The BLEU-4 scores of the two models are compared for generating the binary-value ground truth for the logistic regression classifier.
So instead of using a single DNN as classifier we propose an ensemble of seven independent DNN learners by varying only the input to these DNNs keeping their architecture and intrinsic properties same.
Neural networks have now long been used for solving complex problems of image domain, yet designing the same needs manual expertise.
We incorporate the goodness of both approaches by proposing a convolutional-recurrent encoder for capturing the context information as well as the sequential information from the source sentence.
The idea behind using SincNet filters on the raw speech waveform is to extract more distinguishing frequency-related features in the initial convolution layers of the CNN architecture.
This paper proposes a new probabilistic non-extensive entropy feature for texture characterization, based on a Gaussian information measure.